Transfer Learning: Techniques and Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 November 2025 | Viewed by 950
Special Issue Editors
Interests: industrial deep learning; machine learning; transfer learning; continual learning; lifelong learning; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Transfer learning has become essential in advancing artificial intelligence and deep learning, enabling the effective reuse of knowledge across domains, tasks, and data distributions. It plays a critical role in overcoming challenges such as data scarcity, domain shift, and generalization to unseen environments, making it vital in AI applications.
This Special Issue aims to compile recent advances in transfer learning, including novel methods, theoretical foundations, and practical applications. Topics of interest include, but are not limited to, the following:
- Novel methods and theoretical frameworks for transfer learning and domain adaptation.
- Novel pre-training strategies (e.g., self-supervised, supervised, domain-specific) and their impact on downstream tasks.
- Novel methods for cross-domain, cross-task and cross-modal transfer learning.
- Techniques for dealing with domain shift, catastrophic forgetting, and negative transfer.
- Transfer learning across different data modalities (e.g., vision, text, speech, graphs, time series) or structures.
- Applications of transfer learning to solve real-world challenges (e.g., simulation to reality, medical image analysis, robotics, autonomous systems, computer vision, engineering and Industry 4.0).
- Empirical investigations and benchmarks comparing different transfer learning approaches.
- Investigations into the robustness, fairness, and limitations of transfer learning methods.
- Efficient adaptation methods for large foundation models and LLMs.
We hope to provide a collection of high-quality papers that reflect the current state of the art and emerging research directions in transfer learning.
We look forward to receiving your contributions.
Dr. Hasan Tercan
Dr. Yang Li
Guest Editors
Manuscript Submission Information
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Keywords
- transfer learning
- domain adaptation
- deep learning
- artificial intelligence
- pre-training
- knowledge transfer
- machine learning
- sim-to-real transfer
- transfer learning in continual and dynamic environment
- test-time adaptation
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